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arXiv · 2601.19953

Probabilistic Sensing: Intelligence in Data Sampling

Ibrahim Albulushi·Saleh Bunaiyan·Suraj S. Cheema·Hesham ElSawy·Feras Al-Dirini
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Abstract

Extending the intelligence of sensors to the data-acquisition process - deciding whether to sample or not - can result in transformative energy-efficiency gains. However, making such a decision in a deterministic manner involves risk of losing information. Here we present a sensing paradigm that enables making such a decision in a probabilistic manner. The paradigm takes inspiration from the autonomous nervous system and employs a probabilistic neuron (p-neuron) driven by an analog feature extraction circuit. The response time of the system is on the order of microseconds, over-coming the sub-sampling-rate response time limit and enabling real-time intelligent autonomous activation of data-sampling. Validation experiments on active seismic survey data demonstrate lossless probabilistic data acquisition, with a normalized mean squared error of 0.41%, and 93% saving in the active operation time of the system and the number of generated samples.

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Ibrahim Albulushi, Saleh Bunaiyan, Suraj S. Cheema, Hesham ElSawy, Feras Al-Dirini. 2026-01-27. Probabilistic Sensing: Intelligence in Data Sampling. https://arxiv.org/abs/2601.19953

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